Impugan: Learning Conditional Generative Models for Robust Data Imputation

Incomplete data is a pervasive challenge in real-world applications. This paper introduces Impugan, a conditional Generative Adversarial Network (cGAN) designed for robustly imputing missing values and integrating heterogeneous datasets. Impugan captures nonlinear and multimodal relationships that conventional imputation methods struggle with.